Object detection has received growing interest and attention from academia and industries in recent years. This growing interest and attention stems from an ever-increasing need to efficiently and accurately detect and identify an object of interest (e.g., a person, a vehicle, etc.) in such application fields as security, data mining, or the like.
Given an image, for example, object detection methods may be used to determine a presence and a location (if present) of an object within the image. Because of the complex nature of visual information and presences of an unknown number of objects of different types in the image, however, existing object detection methods normally simplify this detection of an object of interest by making various heuristic assumptions about the object of interest and/or background in the image. Due to this reliance on the heuristic assumptions, false positives (i.e., indicating a detection of an object of interest when the object is not present) and false negatives (i.e., failing to detect an object of interest when the object is present) are usually unavoidable in the existing object detection methods. Furthermore, these heuristic assumptions may be valid in one type of situation (such as certain background) but fail to adapt to another type of situation.